Large-scale machine learning faces a number of fundamental computational challenges, triggered both by the high dimensionality of modern data and the increasing availability of very large training collections. Besides the need to cope with high-dimensional features extracted from images, volumetric data, etc., a key challenge is to develop techniques able to fully leverage the information content and learning opportunities opened by large training collections of millions to billions or more items, with controlled computational resources. Such training volumes can severely challenge traditional statistical learning paradigms based on batch empirical risk minimization.

We present a framework for analyzing the exact dynamics of a class of online learning algorithms in the high-dimensional scaling limit. Our results are applied to two concrete examples: online regularized linear regression and principal component analysis. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measures of the target feature vector and its estimates provided by the algorithms will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE. Numerical solutions of this PDE can be efficiently obtained. These solutions lead to precise predictions of the performance of the algorithms, as many practical performance metrics are linear functionals of the joint empirical measures. In addition to characterizing the dynamic performance of online learning algorithms, our asymptotic analysis also provides useful insights. In particular, in the high-dimensional limit, and due to exchangeability, the original coupled dynamics associated with the algorithms will be asymptotically "decoupled", with each coordinate independently solving a 1-D effective minimization problem via stochastic gradient descent. Exploiting this insight for nonconvex optimization problems may prove an interesting line of future research.

A leading provider of data science education, Springboard was just named one of the best data science bootcamps in the world by SwitchUp for the second year in a row! Springboard recently overhauled the course to give students an even better learning experience via their online, mentor-led curriculum. They took feedback from students, alumni, and mentors, and combined it with deep industry research to make their courses better--here's how: The curriculum now includes cutting edge teachings in deep learning and machine learning. Dig deep into artificial intelligence with new course modules, and learn one of today's most in-demand skills. They partnered with leaders at Datacamp--experts in teaching R and Python--to update the rest of the curriculum too.

Chatbots are used for a variety of tasks: ordering pizza, getting product suggestions via Facebook Messenger and receiving online customer support. But can they cope with death? A three-year clinical study with financial backing of more than $1 million from the National Institutes of Health is exploring whether a chatbot can help terminally ill, geriatric patients with their end-of-life care. Over the next three years, Northeastern University professor Timothy Bickmore and Boston Medical Center doctor Michael Paasche-Orlow will distribute Microsoft Surface tablets preloaded with a chatbot to about 360 patients who have been told they have less than a year to live. Designed in consultation with experts from Boston Medical Center and programmed by Bickmore and other Northeastern University researchers, the chatbot -- which takes the form of a middle-age female digital character -- is preloaded with a number of capabilities.

The Girl Scouts of the USA (GSUSA) announced today a new partnership with Raytheon, an innovator in the cybersecurity space, to further the organization's objective to encourage young women to develop skills in science, technology, engineering, and math, aka STEM. The pair is teaming up to launch the GSUSA's first national computer science program and coding challenge for girls in middle and high school. According to the official release, "the program aims to prepare girls in grades 6-12 to pursue computer science careers in fields such as cybersecurity, artificial intelligence, robotics, and data science." The Girl Scouts are certainly no stranger to the development of STEM skills in young women. The organization partnered with SETI Institute earlier this year to help increase girls' interest in STEM fields.

The Christmas period is typically shrouded in tradition, from the centuries-old folklore of Santa Claus to the classic festive hits that come back year after year. But with technology's grip on society growing ever-stronger, modern gadgetry is bound to change the way we celebrate the holiday season. A new report has looked into how state-of-the-art technology will shape the'Christmas of the future'. The Amazon study, crafted by leading futurists, claims that 3D-printed food and wish lists generated by artificial intelligence will shape the festive period by 2037. A new report has looked into how state-of-the-art technology will shape the'Christmas of the future'.

Chance the Rapper performs in concert on the second day of week two of the Austin City Limits Music Festival at Zilker Park on Oct. 14, 2017 in Austin, Texas. SAN FRANCISCO -- Google is teaming up with Chance the Rapper to bring computer science education to Chicago's public schools. The Internet giant's philanthropic arm Google.org is giving $1 million to Chance the Rapper's SocialWorks organization and $500,000 to the schools. Chicago is the first national school district to mandate computer science education for all students. Chance the Rapper made a surprise appearance at Adam Clayton Powell Jr. Academy on Wednesday where fifth-grade students were working on a coding activity with Google employees as a part of Computer Science Education Week.

In this paper, we propose a new algorithm for learning general latent-variable probabilistic graphical models using the techniques of predictive state representation, instrumental variable regression, and reproducing-kernel Hilbert space embeddings of distributions. Under this new learning framework, we first convert latent-variable graphical models into corresponding latent-variable junction trees, and then reduce the hard parameter learning problem into a pipeline of supervised learning problems, whose results will then be used to perform predictive belief propagation over the latent junction tree during the actual inference procedure. We then give proofs of our algorithm's correctness, and demonstrate its good performance in experiments on one synthetic dataset and two real-world tasks from computational biology and computer vision -- classifying DNA splice junctions and recognizing human actions in videos.

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman's bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g. average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.